Joni Salminen

Fool’s Gold? Developer Dilemmas in a Closed Mobile Application Market Platform

In this paper, we outline some potential conflicts that platform owners and software developers face in mobile application markets. Our arguments are based on comments captured in specialized online discussion forums, in which developers gather to share knowledge and experiences. The key findings indicate conflicts of interests, including 1) intra-platform competition, 2) discriminative promotion, 3) entry prevention, 4) restricted monetization, 5) restricted knowledge sharing, 6) substitution, and 7) strategic technology selection. Opportunistic platform owners may use their power to discriminate between third-part software developers. However, there are also potential strategic solutions that developers can apply; for example diversification (multi-homing), syndication and brand building.

Findings of a User Study of Automatically Generated Personas

We report findings and implications from a semi-naturalistic user study of a system for Automatic Persona Generation (APG) using large-scale audience data of an organizations social media channels conducted at the workplace of a major international corporation. Thirteen participants from a range of positions within the company engaged with the system in a use case scenario. We employed a variety of data collection methods, including mouse tracking and survey data, analyzing the data with a mixed method approach. Results show that having an interactive system may aid in keeping personas at the forefront while making customer-centric decisions and indicate that data-driven personas fulfill information needs of decision makers by mixing personas and numerical data. The findings have implications for the design of persona systems and the use of online analytics data to better understand users and customers.

Viewed by too many or viewed too little: Using information dissemination for audience segmentation: Viewed by Too Many or Viewed Too Little: Using Information Dissemination for Audience Segmentation

The identification of meaningful audience segments, such as groups of users, consumers, readers, audience, etc., has important applicability in a variety of domains, including for content publishing. In this research, we seek to develop a technique for determining both information dissemination and information discrimination of online content in order to isolate audience segments. The benefits of the technique include identification of the most impactful content for analysis. With 4,320 online videos from a major news organization, a set of audience attributes, and more than 58 million interactions from hundreds of thousands of users, we isolate the key pieces of content in terms of identifying audience segments that are both (a) least and most discriminating in terms of audience segments and (b) the least and most impactful. By empirical methods, we show that 25.3 percent of the videos are so widely disseminated (i.e., viewed by so many different segments) that they are non‐discriminatory, while 29.7 percent of the videos are very discriminatory (i.e., can clearly identify one or more audience segments) but their impact is marginal, as the user base is small. Implications are that there are critical values that can be identified to isolate the set of both distinct and impactful content in a given data set of online content. We demonstrate the utility of this line of analysis by using the approach to identify critical cut‐off values for dynamic persona generation.

With or Without Super Platforms? Analyzing Online Publishers’ Strategies in the Game of Traffic

Given the dominance of online platforms in attracting consumers and advertisers, online publishers are squeezed between declining traffic and advertising revenues from their website content. In turn, super platforms, the dominant content dissemination platforms, such as Google and Facebook, are monetizing online content at the expense of publishers by selling ad impressions in advertising auctions. In this work, we analyze publishers’ possibilities of forming a coalition and show that, under a set of assumptions, the optimal strategy for publishers is cooperation against a super platform rather than posting content on the super platform. Not choosing to publish on a super platform can yield the whole coalition more traffic, enabling some individual publishers to recoup the lost traffic. We further show that if the coalition does not forbid diversification, most publishers choose both coalition and super platform.

Persona Perception Scale: Developing and Validating an Instrument for Human-Like Representations of Data

Personas are widely used in software development, system design, and HCI studies. Yet, their evaluation is difficult, and there are no recognized and validated measurement scales to date. To improve this condition, this research develops a persona perception scale based on reviewing relevant literature. We validate the scale through a pilot study with 19 participants, each evaluating three personas (57 evaluations in total). This is the first reported effort to systematically develop and validate an instrument for persona perception measurement. We find the constructs and items of the scale perform well, with factor loadings ranging between 0.60 and 0.95. Reliability, measured as Cronbachs Alpha, is also satisfactory, encouraging us to pursue the use of the scale with a larger sample in future work.

Fixation and Confusion: Investigating Eye-tracking Participants' Exposure to Information in Personas

To more effectively convey relevant information to end users of persona profiles, we conducted a user study consisting of 29 participants engaging with three persona layout treatments. We were interested in confusion engendered by the treatments on the participants, and conducted a within-subjects study in the actual work environment, using eye-tracking and talk-aloud data collection. We coded the verbal data into classes of informativeness and confusion and correlated it with fixations and durations on the Areas of Interests recorded by the eye-tracking device. We used various analysis techniques, including Mann-Whitney, regression, and Levenshtein distance, to investigate how confused users differed from non-confused users, what information of the personas caused confusion, and what were the predictors of confusion of end users of personas. We consolidate our various findings into a confusion ratio measure, which highlights in a succinct manner the most confusing elements of the personas. Findings show that inconsistencies among the informational elements of the persona generate the most confusion, especially with the elements of images and social media quotes. The research has implications for the design of personas and related information products, such as user profiling and customer segmentation.

Customer segmentation using online platforms: isolating behavioral and demographic segments for persona creation via aggregated user data

We propose a novel approach for isolating customer segments using online customer data for products that are distributed via online social media platforms. We use non-negative matrix factorization to first identify behavioral customer segments and then to identify demographic customer segments. We employ a methodology for linking the two segments to present integrated and holistic customer segments, also known as personas. Behavioral segments are generated from customer interactions with online content. Demographic segments are generated using the gender, age, and location of these customers. In addition to evaluating our approach, we demonstrate its practicality via a system leveraging these customer segments to automatically generate personas, which are fictional but accurate representations of each integrated behavioral and demographic segment. Results show that this approach can accurately identify both behavioral and demographical customer segments using actual online customer data from which we can generate personas representing real groups of people.

Platform as a Social Contract: An Analytical Framework for Studying Social Dynamics in Online Platforms

In addition to formal terms of service and contracts between platform owners, users, and other stakeholders, there can be seen an implicit social contract taking place in online platforms, and influencing the social dynamics, such as trust, expectations, and perceived social justice, taking place within platforms, and driving their growth and success in the background. This paper examines the nature of that social contract, to better understand the complex social dynamics taking place in online platforms. To accomplish that objective, we draw from classic Enlightenment thinkers, e.g., Rousseau, Locke, and Hobbes, to analyze key aspects of social contracts, which we define as the alignment of stakeholder interests, stakeholder support, economic and social justice, and transparency of expectations. As our main contribution, we develop a conceptual framework for the analysis of platforms based on social contract theory, the Platforms as a Social Contract framework. The applicability of the framework is illustrated through a case analysis of YouTube, a popular online content platform. The rich understanding provided by the social contract perspective, embodied in our framework, entails many potential advantages to platform owners, including understanding user motivations and reactions so that effective platform governance with maintaining a sustainable solution to the chicken-and-egg problem becomes possible. While individual platforms may come and go, each faces the same fundamental social dynamics that can be explained and understood by applying the social contract framework presented in this research. This research shows how the framework can be used for analysis of online platforms, as well as suggests future research avenues for developing deeper understanding of platforms as a social contract.

Neural Network Hate Deletion: Developing a Machine Learning Model to Eliminate Hate from Online Comments

We propose a method for modifying hateful online comments to non-hateful comments without losing the understandability and original meaning of the comments. To accomplish this, we retrieve and classify 301,153 hateful and 1,041,490 non-hateful comments from Facebook and YouTube channels of a large international media organization that is a target of considerable online hate. We supplement this dataset by 10,000 Reddit comments manually labeled for hatefulness. Using these two datasets, we train a neural network to distinguish linguistic patterns. The model we develop, Neural Network Hate Deletion (NNHD), computes how hateful the sentences of a social media comment are and if they are above a given threshold, it deletes them using a language dependency tree. We evaluate the results by comparing crowd workers’ perceptions of hatefulness and understandability before and after transformation and find that our method reduces hatefulness without resulting in a significant loss of understandability. In some cases, removing hateful elements improves understandability by reducing the linguistic complexity of the comment. In addition, we find that NNHD can satisfactorily retain the original meaning on average but is not perfect in this regard. In terms of practical implications, NNHD could be used in social media platforms to suggest more neutral use of language to agitated online users.

Open-Source Software Entrepreneurial Business Modelling

This poster aims to facilitate business planning of bootstrapping entrepreneurs who are developing a high-tech business by open-source approach. It draws on scholarly works on business modelling and open-source software to provide a practical tool for entrepreneurs establishing a business by open-source approach. Built on top of established business modelling frameworks, the Open-Source Software Entrepreneurial Business Modelling (OSS_EBM) can be a useful strategic management and entrepreneurial tool. It enables strategists and entrepreneurs to describe, design, challenge, invent, brainstorm, pivot, analyze and improve upon open-source business models.